Porosity and permeability are the most important attributes of reservoir rock. They determine the amount of fluid a rock can contain and the rate at which that fluid can be produced (Dickey, 1986). Porosity is defined as the "property possessed by a rock of containing interstices, without regard to size, shape, or interconnection of openings. It is expressed as the percentage of the total (bulk) volume occupied by the interstices" (API, 1941). Permeability is a measure of the ability of a rock to transmit fluids. A detailed discussion of porosity and permeability and the measurement of these rock properties is the subject of Chapter 11.
Accurate porosity values are critical to predrill evaluation of resources in potential reservoirs (potentially recoverable hydrocarbons in undiscovered fields are called resources, not reserves). Once a discovery is made, reservoir rock properties (porosity/permeability) are used to establish pore volume, hydrocarbon pore volume, recoverable reserves, production rates, well spacings, fluid injection rates, etc. The need for accurate estimates of rock properties extends to the whole "life cycle" (discovery, appraisal, planning, development, and management) of a reservoir (Table 1-1). Porosity and permeability data and estimates provide essential input to mathematical reservoir models, used to evaluate both new and mature fields, and to determine the most efficient method of economic development.
Porosity and permeability are also key parameters in basin modeling. Such models help in the interpretation of sedimentary, hydrodynamic, geochemical, and tectonic processes which affected a given area over geologic time. Understanding of processes controlling basin formation
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Reservoir Quality Assessment and Prediction in Clastic Rocks
This course is designed to emphasize the following topics: (1) Historical perspective on previous and current empirical, and geochemical methods of reservoir quality prediction; (2) Overview of diagenetic processes which significantly impact reservoir quality and those factors which act as major controls on those processes; (3) Proper design of a comprehensive or limited-focus predictive analysis of reservoir quality; (4) Methodologies for the accurate measurement of all major dependent and independent variables; (5) Data analysis techniques involved in quality control and the assessment of variability prior to performing multivariate regression; (6) Steps involved in the generation of a multivariate regression to insure that the model developed provides maximum accuracy using a minimum number of independent variables; (7) Case histories from a variety of settings illustrating application of the recommended approach to reservoir quality prediction.